SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 3871–3880 of 15113 papers

TitleStatusHype
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning—0
By Fair Means or Foul: Quantifying Collusion in a Market Simulation with Deep Reinforcement Learning—0
Rectifying Reinforcement Learning for Reward Matching—0
Learning the Target Network in Function Space—0
Reinforcement Learning as a Robotics-Inspired Framework for Insect Navigation: From Spatial Representations to Neural Implementation—0
Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems—0
MOSEAC: Streamlined Variable Time Step Reinforcement LearningCode0
MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading—0
NeoRL: Efficient Exploration for Nonepisodic RL—0
A Fast Convergence Theory for Offline Decision Making—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified